Filling in the gaps: The development of contour interpolation
Bibliographic record
Abstract
Adults can see bounded figures even when local image information fails to provide cues to specify their edges (e.g., Ginsburg, 1975). Such contour interpolation leads to the perception of subjective contours — edges perceived in the absence of any physically present discontinuities (e.g., Kanizsa, 1955). We examined the development of sensitivity to shape formed by subjective contours and the effect thereon of support ratio (the ratio of the physically specified contours to the total edge length). Children aged 6, 9, and 12 years and adults (n = 20 per group) performed a shape discrimination task. Sensitivity to shape formed by luminance-defined contours was compared to shape formed by subjective contours with high support ratio (interpolation of contours between the inducers was over a small distance relative to the size of the inducers) or low support ratio (interpolation of contours was over a larger distance). Results reveal a longer developmental trajectory for sensitivity to shape formed by subjective contours compared to shape formed by luminance-defined contours: only by 12 years of age were children as sensitive as adults to subjective contours (pp[[gt]].1). The protracted development of sensitivity to subjective contours is consistent with evidence for delayed development of feedback connections from V2 to V1 (Burkhalter, 1993) known to be important for the perception of subjective contours (e.g., Ffytche & Zeki, 1996). As in adults, sensitivity to subjective contours was better with higher support ratio by 9 years of age (psp[[gt]].1). The results suggest that, over development, support ratio becomes a reliable predictor for interpolation, so that contours that are more likely to reflect real objects‘ contours (i.e., highly supported contours) are more easily interpolated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".